Weakly Supervised Classification Using Group-Level Labels
This addresses the challenge of data annotation costs and privacy concerns in domains like land cover mapping, but it is incremental as it builds on existing noisy label regularization methods.
The paper tackles the problem of limited labeled data by using group-level binary labels as weak supervision to train instance-level binary classification models, achieving utility in land cover mapping experiments with and without class imbalance.
In many applications, finding adequate labeled data to train predictive models is a major challenge. In this work, we propose methods to use group-level binary labels as weak supervision to train instance-level binary classification models. Aggregate labels are common in several domains where annotating on a group-level might be cheaper or might be the only way to provide annotated data without infringing on privacy. We model group-level labels as Class Conditional Noisy (CCN) labels for individual instances and use the noisy labels to regularize predictions of the model trained on the strongly-labeled instances. Our experiments on real-world application of land cover mapping shows the utility of the proposed method in leveraging group-level labels, both in the presence and absence of class imbalance.